The importance of individual heterogeneity in the composition of measures of socioeconomic inequality in health: an approach based on quantile regression
Bibliographic record
Abstract
This paper shows how recently developed regression-based methods for the \ndecomposition of health inequality can be extended to incorporate individual \nheterogeneity in the responses of health to the explanatory variables. We illustrate our \nmethod with an application to the Canadian NPHS of 1994. Our strategy for the \nestimation of heterogeneous responses is based on the quantile regression model. The \nresults suggest that there is an important degree of heterogeneity in the association of \nhealth to explanatory variables which, in turn, accounts for a substantial percentage of \ninequality in observed health. A particularly interesting finding is that the marginal \nresponse of health to income is zero for healthy individuals but positive and significant \nfor unhealthy individuals. The heterogeneity in the income response reduces both \noverall health inequality and income related health inequality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".